Real-time transmission switching with neural networks

Bugaje, A.-.A.B.; Cremer, Jochen; Strbac, Goran

doi:10.1049/gtd2.12698

Real-time transmission switching with neural networks

Title

Real-time transmission switching with neural networks

Author

Bugaje, A.-.A.B. (Imperial College London)
Cremer, Jochen (TU Delft Intelligent Electrical Power Grids)
Strbac, Goran (Imperial College London)

Date

2022

Abstract

The classical formulation of the transmission switching problem as a mixed-integer problem is intractable for large systems in real-time control settings. Several heuristics have been proposed in the past to speed up the computation time, which only limits the number of switchable lines. In this paper, a real-time switching heuristic based on neural networks that provides almost instantaneous switching actions, are presented. The findings are shown on case studies of the IEEE 118-bus test system, and the results show that the proposed heuristic is robust to out of distribution data. Additionally, the proposed heuristic has significant computational savings while all other performance metrics like accuracy are similar to state-of-the-art machine learning methods proposed for transmission switching.

Subject

artificial intelligence
power transmission control
real-time systems

To reference this document use:

http://resolver.tudelft.nl/uuid:fe7806d3-c110-4fbf-b450-504bb9d08d93

DOI

https://doi.org/10.1049/gtd2.12698

ISSN

1751-8687

Source

IET Generation, Transmission and Distribution, 17 (3), 696-705

Part of collection

Institutional Repository